Maria Jose Jimenez

The design and optimisation of energy efficiency measures in buildings are often based on simulation models, which must be validated with experimental measurements to ensure a reliable representation of the building’s actual behaviour. One of the main challenges in this process is minimizing uncertainties associated with input variables, as any deviation can lead to significant discrepancies from the design case. The main objective of this study is to quantify the impact of input variable variations on the heating and cooling loads of an office model. For this purpose, a validated dynamic simulation methodology has been developed and implemented in several phases: monitoring of the office space and boundary conditions; dynamic modelling of the office; validation of the simulation model using experimental data; and sensitivity study through parametric and influence analysis. As a result of this methodology, the most influential variables affecting three annual series of thermal demand have been identified. In this case study, it is concluded that accurate definition of seasonal set-point temperatures, climate files, and infiltration rates are crucial for obtaining accurate thermal demand estimates. Conversely, variations in occupancy schedules and ground temperature profiles have a lower impact, allowing for less accuracy in their input definition without compromising the reliability of the results.

There still exists a considerable difference when comparing the real and the design energy consumption of buildings. The difference between the design and the real building envelope energy performance is one of its main reasons. The building envelope can be characterised through the individual characterisation of its different building envelope components such as opaque walls or windows. Therefore, the estimation of parameters such as their transmission heat transfer coefficient (UA) and their solar aperture (gA) is usually implemented. Although building components have been analysed over the years, the thermal characteristics of buildings have mainly been estimated through steady-state laboratory tests and simplified calculation/simulation procedures based on theoretical data. The use of inverse modelling based on registered dynamic data has also been used; however, unfortunately, the models used tend to significantly simplify or neglect the solar radiation effect on the inner surface heat flux of opaque building envelope elements. Therefore, this work presents an experimental, dynamic and inverse modelling method that accurately models non-linear phenomena through the use of a user-friendly simulation programme (LORD). The method is able to analyse in detail the effect of the solar radiation on the inner surface heat flux of opaque building envelope elements, without the necessity of knowing their constructive details or thermal properties. The experiment is performed in a fully monitored test box, where different models are tested with different opaque walls to find the best fit. Finally, the solar irradiance signal is removed from the best models so as to accurately quantify the weight of the solar radiation on the inner surface heat flux of each wall for two extreme periods, one for sunny summer days and other for cloudy winter days.

The measurement of the CO₂ concentration has a wide range of applications. Traditionally, it has been used to assess air quality, with other applications linked to the experimental assessment of occupancy patterns and air renewal rates. More recently, the worldwide dissemination of COVID-19 establishing a relationship between infection risk and the mean CO₂ level has abruptly led to the measurement of the CO₂ concentration in order to limit the spread of this respiratory disease in the indoor environment. Therefore, the extensive application of this measurement outside of traditional air quality assessment requires an in-depth analysis of the suitability of these sensors for such modern applications. This paper discusses the performance of an array of commercial wall-mounted CO₂ sensors, focusing on their application to obtain occupancy patterns and air renovation rates. This study is supported by several long-term test campaigns conducted in an in-use office building located in south-eastern Spain. The results show a spread of 19–101 ppm, with a drift of 28 ppm over 5 years, an offset of 2–301 ppm and fluctuations up to 80 ppm in instantaneous measurements not related to concentration changes. It is proposed that values averaged over 30 min, using a suitable reference value, be used to avoid erroneous results when calibration is not feasible.

Building occupancy is one of the relevant variables to understand the energy performance of buildings and to reduce the current gap between simulation-based and actual energy performance. In this study, the occupancy of a classroom in an educational center monitored over a full year was experimentally assessed. The classroom had different occupancy levels during the school year, with a theoretical minimum of eleven students, and no occupancy during vacations and weekends. Different variables such as indoor air temperature, relative humidity, CO₂ concentration, overall electrical energy consumption of the educational center, electrical energy consumption of the building in which the monitored classroom is located, and heating energy consumption were recorded. We analyzed which of these variables were possible indicators of classroom occupancy, using the school timetable as a theoretical reference value for the validation of the results. Based on previous studies, one-hour moving averages are used to better identify the occupancy patterns by smoothing the fluctuations that are not a consequence of a change in the classroom occupancy. Histograms of each variable are used to identify the variable ranges associated within the occupancy: occupied or empty. The concentration of CO₂ and electric measurements, identified in previous works as suitable to assess the occupancy patterns of rooms like offices with lower levels of occupancy, are recognized as potential occupancy indicators. It is therefore concluded that a higher level of space occupancy does not affect the result, and the same variables are identified as potential occupancy indicators.

This paper reports the analysis of the feasibility to characterise the air leakage and the mechanical ventilation avoiding the intrusiveness of the traditional measurement techniques of the corresponding indicators in buildings. The viability of obtaining the air renovation rate itself from measurements of the concentration of the metabolic CO₂, and the possibilities to express this rate as function of other climatic variables, are studied. N₂O tracer gas measurements have been taken as reference. A Test Cell and two full size buildings, with and without mechanical ventilation and with different levels of air leakage, are considered as case studies. One-month test campaigns have been used for the reference N₂O tracer gas experiments. Longer periods are available for the analysis based on CO₂ concentration. When the mechanical ventilation is not active, the results indicate significant correlation between the air renovation rate and the wind speed. The agreement between the N₂O reference values and the evolution of the metabolic CO₂ is larger for larger initial values of the CO₂ concentration. When the mechanical ventilation is active, relevant variations have been observed among the N₂O reference values along the test campaigns, without evidencing any correlation with the considered boundary variables.

This paper reports the application of RC dynamic models for assessing thermal performance of buildings from in-situ tests (obtaining the U value for the walls, and the UA value and gA value for the whole buildings). The following aspects which are relevant to this approach have been systematically analyzed: The effect of the solar radiation on the heat flux through the opaque walls versus the performance of the models including this effect, the optimum number of nodes required to represent the thermal systems, the assignment of inputs and outputs and the length of the test period. Additionally, several options modelling relevant effects using unmeasured variables were studied to evaluate the feasibility to reduce the cost and intrusiveness of the measurement devices required to obtain accurate results. Data series recorded under different experimental conditions were considered to analyze the robustness and validity of the results. The performance of the models for each of these different test conditions is discussed. The uncertainties estimated using the described method for the U values of the opaque walls, and the UA and gA values of the whole building, are, respectively, 2.8%, 4.2% and 2.3%. The feasibility to model relevant effects using unmeasured variables has been demonstrated. A simplified and well-known building has been used as a case study, reinforcing and complementing the validation criteria.